In our previous works [1]}, [2]}, a narrow band deep filtering (NBDF) method was proposed, which inputs the frequency-wise multichannel signals to a long short-term memory (LSTM) network, and predicts the clean speech at the corresponding frequency. NBDF has achieved excellent speech enhancement performance in terms of both human perception and ASR, thus it is used as the baseline network in this paper. We propose three variants of NBDF in this work to account for the varying number of microphones, namely channel-padding NBDF (CP-NBDF), channel-convolutional NBDF (CC-NBDF), and pair-wise NBDF (PW-NBDF). A large number of virtual microphone arrays with simulated RIRs are used for training. Multichannel signals are generated by convolving RIRs with single-channel speech clips. By using the toolkit presented in [3]} and [4]}, multichannel spatially diffuse babble, white and spatially correlated wind noise are generated respecting geometries of those arrays. The multichannel speech enhancement network normally recovers the monaural clean speech of a reference channel. We propose a simple and effective network training scheme to allow the users to specify a reference channel for their own microphone array during test. Besides, a magnitude augmentation scheme is proposed to account for the fact that different microphone arrays could have different configurations in terms of recording volume or frequency response. Overall, combining all of these, the network is trained to learn the universal information for speech enhancement that is available in any microphone array. Experiments demonstrate that our proposed networks have excellent generalization capability to various unseen simulated and real arrays. Moreover, they achieve better speech enhancement performance than various beamforming methods and the advanced FaSNet-TAC method [5]}.
